E-Commerce Data Analysis Using SQL & Python

This project performs an end-to-end analysis of an e-commerce dataset using MySQL and Python to extract business insights and growth metrics.

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📌 Tools Used

• MySQL Workbench
• Python (Pandas, Matplotlib)
• Google Colab
• GitHub

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Dataset

• Raw datasets not included due to GitHub size limits.

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📊 Analysis Performed

• Customer distribution analysis
• Monthly order trends (2018)
• Revenue contribution by category
• Seller performance ranking
• Year-over-year sales growth
• Customer retention (6-month repeat purchase)
• Price vs demand correlation

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📁 Project Structure

• sql/ → SQL queries (basic to advanced)
• sql_results/ → Exported SQL outputs
• python/ → Jupyter notebook with visualizations
• visuals/ → Generated charts
• presentation/ → Final PowerPoint presentation

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🔎 Key Insights

• Revenue concentrated in limited product categories
• Installment payments are widely used (~49%)
• Strong growth from 2016 to 2018
• Repeat customer behavior observed
• Price shows weak correlation with purchase frequency

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📈 Business Impact

This project simulates real-world e-commerce analytics and demonstrates SQL querying, time-series analysis, correlation analysis, and business interpretation skills.